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High-frequency sampling of point samples of lump ores

Point sampling of large-lump ores at mines and processing plants is usually performed manually. At the same time, small pieces are beaten off from large pieces, or small fractions of ore are taken into a point sample. Such point sampling is accompanied by a systematic error of 7−10%. The number of spot samples from large-lump ores can be 2−4 per shift. A small number of point samples leads to a large random error of 5 to 9%. In order to reduce the complexity of sampling and reduce the random error, it is proposed to select narrow classes of small size of the processed ore that appear in it during crushing and transportation into a point sample. The limiting variant of the narrow class is the dust fraction minus 0.1 mm, completely natural for analysis, taken from the ore stream during the shift. In this case, high-frequency sampling of point samples (dust particles) is carried out. Several million such spot samples are taken into the combined replacement sample. The relative random error of the replacement sample will not exceed 0.5%. To eliminate the systematic error of the sample, represented by a narrow class of size, it is necessary to find the conversion factor for the average monthly result. The conversion factor can be either greater or less than one, depending on the strength of the minerals containing the component being determined. The implementation of high-frequency lump testing will allow solving the time-consuming procedure of testing lump ores in the most economical way.

Keywords: bulk sampling, systematic error, random error, conversion factor, point samples, coarse ores, dust fractions, replaceable sample.
For citation:

Kozin V. Z. , Komlev A. S. High-frequency sampling of point samples of lump ores. MIAB. Mining Inf. Anal. Bull. 2024;(1-1):153—166. [In Russ]. DOI: 10.25018/0236_1493_2024_011_0_153.

Acknowledgements:

The study was carried out with the support of the Ministry of Science and Higher Education of the Russian Federation in accordance with the state task No. 0833−2023−0004 for the Ural State Mining University.

Issue number: 1
Year: 2024
Page number: 153-166
ISBN: 0236-1493
UDK: 622.7.092
DOI: 10.25018/0236_1493_2024_011_0_153
Article receipt date: 15.05.2023
Date of review receipt: 24.08.2023
Date of the editorial board′s decision on the article′s publishing: 10.12.2023
About authors:

Kozin V. Z.1, Head of the Department of Mineral Processing, Dean of the Faculty of Mining and Mechanics, Dr. Sci. (Eng.), Professor, ORCID ID: 0000-0001-7184-919X, е-mail: gmf.dek@ursmu.ru;
Komlev A. S.1, Cand. Sci. (Eng.), Senior Researcher, ORCID ID: 0000-0002-2484-2726, е-mail: tails2002@inbox.ru (сorresponding author);
1 Ural State Mining University, Russia, 620144, Yekaterinburg, Kuibyshev str., 30.

 

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Bibliography:

1. Glazatov A. N., Lunev V. Yu., Parievsky E. V., Danchenko E. V. Experimental assessment of the reliability of the result of ore testing at the Talnakh processing plant. Non–ferrous metals. 2022, no. 2. pp. 78–86. [In Russ]. DOI: 10.17580/tsm.2022.02.10.

2. Kozin V. Z. Testing of mineral raw materials. Yekaterinburg, Publishing house of UGSU, 2011, 315 p. [In Russ].

3. Napier-Munn T. J., Whiten W. J., Faramarzi F. Bias in manual Sampling of rock particles. Minerals Engineering. 2020, vol. 153, article 106260.

4. Kejonen I., Haavisto O, Martikainen J., Suontaka V, Musuku B. Improving grade control efficiency with rapid on-line elemental analysis. Minerals Engineering. 2018, vol. 124, pp. 68–73.

5. Geraskin V. M. Development of automation and information technologies at the Gaisky GOK. Mining Magazine. 2019, no. 7, pp. 40–42. [In Russ].

6. Temerbekova B. M. Application of the method of revealing the systematic error of integral measurements of technological parameters in complex technological processes and industries. Non–ferrous Metals. 2022, no. 5, pp. 79–86. [In Russ]. DOI: 10.17580/ tsm.2022.05.11.

7. Morozov V. V., Topchaev V. P., Ulitenko K. Ya., Ganbaatar V., Dalgerbat L. Development and application of automated control systems for mineral processing processes. Moscow, Publ. house «Ore and Metals», 2013, 508 p. [In Russ].

8. Abrarov A. D., Datskevich M. S., Chikildin D. E., Fedotov D. N. System of optimization of the process of collective flotation of the Talnakh processing plant based on machine learning algorithms. Non–ferrous metals. 2022, no. 2, pp. 87–93. [In Russ]. DOI: 10.17580/ tsm.2022.02.11.

9. Yakimov I. S., Bezrukova O. E., Dubinkin P. S., Shimansky A. F. X-ray technological control at the enterprises of the mining and metallurgical complex of non-ferrous metallurgy. Non–ferrous Metals. 2019, no. 9, pp. 56–62. [In Russ]. DOI: 10.17580/tsm.2019.09.09.

10. Varlamova S. A., Zatonsky A. V., Fedoseeva K. A. Investigation of sensitivity to illumination of the method of glare recognition of potassium foam flotation machines. Ore dressing. 2021, no. 6, pp. 29–33. [In Russ]. DOI: 10.17580/or.2021.06.05.

11. Lyapin A. G. Innovations of technical control. Mining magazine. 2019, no. 7, pp. 30–31. [In Russ].

12. Morozov V. V., Khurelchuluun I., Dalgerbat L. Control of crushing and screening processes using visiometric ore analysis. Non–ferrous Metals. 2021, no. 7, pp. 17–23. [In Russ]. DOI: 10.17580/tsm.2021.07.01.

13. Engströrn K. Esbensen K. H. Evaluation of sampling systems in iron concentrating and pelletizing processes — Quantification of Total Sampling Error (TSE) vs. process variation. Minerals Engineering. 2018, vol. 116, pp. 203–208.

14. Ramsey M. H., Ellison S. L. R., Rostron P.et al. Measurement uncertainty arising from Sampling: a guide to methods and approach, 2nd Edition. Eurachem. 2019. 109 p.

15. Sona M., Dube J-S., Sampling particulate matter for analysis — Controlling uncertainty and bias using the theory of sampling. Analytica Chimica Acta. 2021, vol. 1185. https://doi.org/10.1016/j.aca.2021.338982.

16. Kozin V. Z., Komlev A. S. Testing of large-batch products. MIAB. Mining Inf. Anal. Bull. 2020, no. 3−1, pp. 410–421. [In Russ]. DOI: 10.25018/0236-1493-2020-21−0-410−421.

17. Larionov A. N., Kalinichenko L. S., Ryazanov M. A. Dry ore preparation technologies as a means of increasing the efficiency of separation processes. Rational development of mineral resources. 2015, no. 5−6, pp. 51–59. [In Russ].

18. Fedotov P. K., Senchenko A. E., Fedotov K. V., Burdonov A. E. Influence of ore destruction method on percolation leaching efficiency. Ore dressing. 2021, no. 2, pp. 15–20. [In Russ]. DOI: 10.17580/or.2021.02.03.

19. Tsypin E. F., Efremova T. A., Ovchinnikova T. Yu. Modeling of the X-ray fluorescence separation process. MIAB. Mining Inf. Anal. Bull. 2022, no. 11−1, pp. 127–139. [In Russ]. DOI: 10.25018/0236_1493_2022_111_0_127.

20. Pelevin A. E. Enrichment of weakly magnetic minerals in roller separators with a system of permanent magnets. MIAB. Mining Inf. Anal. Bull. 2022, no. 11−1, pp. 155–168. [In Russ]. DOI: 10.25018/0236_1493_2022_111_0_155.

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